Get Dataset

Retrieve a specific dataset by ID with complete details

Overview

The get() method allows you to retrieve a specific dataset by its ID, returning complete dataset information including metadata, log count, and timestamps.

Method Signature

Synchronous

client.datasets.get(
    dataset_id: str,
    **kwargs
) -> Dataset

Asynchronous

await client.datasets.aget(
    dataset_id: str,
    **kwargs
) -> Dataset

Parameters

dataset_id · str · required

The unique identifier of the dataset to retrieve.

Returns

Returns a Dataset object with the following structure:

{
    "id": "dataset_123456789",
    "name": "Customer Support Dataset",
    "description": "Collection of customer support conversations for training",
    "log_count": 1250,
    "created_at": "2024-01-15T10:30:00Z",
    "updated_at": "2024-01-20T14:45:00Z",
    "metadata": {
        "category": "support",
        "purpose": "training",
        "version": "1.0",
        "quality_level": "high",
        "language": "english",
        "owner": "data_team",
        "tags": ["support", "training", "quality"]
    }
}

Examples

Basic Dataset Retrieval

from keywordsai import KeywordsAI

client = KeywordsAI(api_key="your-api-key")

# Get a specific dataset
dataset_id = "dataset_123456789"
dataset = client.datasets.get(dataset_id)

print(f"Dataset: {dataset.name}")
print(f"Description: {dataset.description}")
print(f"Log Count: {dataset.log_count}")
print(f"Created: {dataset.created_at}")
print(f"Last Updated: {dataset.updated_at}")

Accessing Dataset Details

# Get dataset and access all properties
dataset = client.datasets.get("dataset_123456789")

# Basic information
print(f"ID: {dataset.id}")
print(f"Name: {dataset.name}")
print(f"Description: {dataset.description}")

# Statistics
print(f"\nStatistics:")
print(f"  Total logs: {dataset.log_count:,}")

# Timestamps
from datetime import datetime
created = datetime.fromisoformat(dataset.created_at.replace('Z', '+00:00'))
updated = datetime.fromisoformat(dataset.updated_at.replace('Z', '+00:00'))

print(f"\nTimestamps:")
print(f"  Created: {created.strftime('%Y-%m-%d %H:%M:%S')}")
print(f"  Updated: {updated.strftime('%Y-%m-%d %H:%M:%S')}")
print(f"  Age: {(datetime.now(created.tzinfo) - created).days} days")

# Metadata
print(f"\nMetadata:")
for key, value in dataset.metadata.items():
    print(f"  {key}: {value}")

Error Handling for Missing Datasets

from keywordsai.exceptions import NotFoundError

def get_dataset_safely(dataset_id):
    try:
        dataset = client.datasets.get(dataset_id)
        return dataset
    except NotFoundError:
        print(f"Dataset {dataset_id} not found")
        return None
    except Exception as e:
        print(f"Error retrieving dataset: {e}")
        return None

# Safe retrieval
dataset = get_dataset_safely("dataset_123456789")
if dataset:
    print(f"Found dataset: {dataset.name}")
else:
    print("Dataset not available")

Asynchronous Retrieval

import asyncio

async def get_dataset_async(dataset_id):
    client = KeywordsAI(api_key="your-api-key")
    
    try:
        dataset = await client.datasets.aget(dataset_id)
        print(f"Async retrieved: {dataset.name}")
        print(f"  Logs: {dataset.log_count}")
        print(f"  Category: {dataset.metadata.get('category', 'N/A')}")
        return dataset
    except Exception as e:
        print(f"Async error: {e}")
        return None

# Run async retrieval
dataset = asyncio.run(get_dataset_async("dataset_123456789"))

Batch Retrieval

def get_multiple_datasets(dataset_ids):
    datasets = []
    failed_ids = []
    
    for dataset_id in dataset_ids:
        try:
            dataset = client.datasets.get(dataset_id)
            datasets.append(dataset)
            print(f"✓ Retrieved: {dataset.name}")
        except NotFoundError:
            failed_ids.append(dataset_id)
            print(f"✗ Not found: {dataset_id}")
        except Exception as e:
            failed_ids.append(dataset_id)
            print(f"✗ Error with {dataset_id}: {e}")
    
    print(f"\nSummary:")
    print(f"  Successfully retrieved: {len(datasets)}")
    print(f"  Failed: {len(failed_ids)}")
    
    return datasets, failed_ids

# Retrieve multiple datasets
dataset_ids = [
    "dataset_123456789",
    "dataset_987654321",
    "dataset_555666777"
]

datasets, failed = get_multiple_datasets(dataset_ids)

Async Batch Retrieval

import asyncio

async def get_multiple_datasets_async(dataset_ids):
    client = KeywordsAI(api_key="your-api-key")
    
    async def get_single_dataset(dataset_id):
        try:
            dataset = await client.datasets.aget(dataset_id)
            return {"success": True, "dataset": dataset, "id": dataset_id}
        except Exception as e:
            return {"success": False, "error": str(e), "id": dataset_id}
    
    # Create tasks for all datasets
    tasks = [get_single_dataset(dataset_id) for dataset_id in dataset_ids]
    
    # Wait for all tasks to complete
    results = await asyncio.gather(*tasks)
    
    # Separate successful and failed retrievals
    successful = [r["dataset"] for r in results if r["success"]]
    failed = [(r["id"], r["error"]) for r in results if not r["success"]]
    
    print(f"Async batch retrieval completed:")
    print(f"  Successful: {len(successful)}")
    print(f"  Failed: {len(failed)}")
    
    for dataset in successful:
        print(f"  ✓ {dataset.name}: {dataset.log_count} logs")
    
    for dataset_id, error in failed:
        print(f"  ✗ {dataset_id}: {error}")
    
    return successful, failed

# Run async batch retrieval
dataset_ids = [
    "dataset_123456789",
    "dataset_987654321",
    "dataset_555666777"
]

successful, failed = asyncio.run(get_multiple_datasets_async(dataset_ids))

Advanced Usage

Dataset Information Summary

def summarize_dataset(dataset_id):
    try:
        dataset = client.datasets.get(dataset_id)
        
        # Calculate age
        from datetime import datetime
        created = datetime.fromisoformat(dataset.created_at.replace('Z', '+00:00'))
        age_days = (datetime.now(created.tzinfo) - created).days
        
        # Determine size category
        if dataset.log_count < 100:
            size_category = "Small"
        elif dataset.log_count < 1000:
            size_category = "Medium"
        elif dataset.log_count < 10000:
            size_category = "Large"
        else:
            size_category = "Very Large"
        
        # Extract key metadata
        category = dataset.metadata.get("category", "Unknown")
        purpose = dataset.metadata.get("purpose", "Unknown")
        quality = dataset.metadata.get("quality_level", "Unknown")
        
        summary = {
            "id": dataset.id,
            "name": dataset.name,
            "category": category,
            "purpose": purpose,
            "size_category": size_category,
            "log_count": dataset.log_count,
            "age_days": age_days,
            "quality_level": quality,
            "last_updated_days_ago": (datetime.now(created.tzinfo) - 
                                    datetime.fromisoformat(dataset.updated_at.replace('Z', '+00:00'))).days
        }
        
        print(f"Dataset Summary: {dataset.name}")
        print(f"  Category: {category} | Purpose: {purpose}")
        print(f"  Size: {size_category} ({dataset.log_count:,} logs)")
        print(f"  Age: {age_days} days | Quality: {quality}")
        print(f"  Last updated: {summary['last_updated_days_ago']} days ago")
        
        return summary
        
    except Exception as e:
        print(f"Error summarizing dataset {dataset_id}: {e}")
        return None

# Summarize a dataset
summary = summarize_dataset("dataset_123456789")

Dataset Validation

def validate_dataset(dataset_id):
    try:
        dataset = client.datasets.get(dataset_id)
        
        validation_results = {
            "dataset_id": dataset.id,
            "name": dataset.name,
            "issues": [],
            "warnings": [],
            "score": 100
        }
        
        # Check required fields
        if not dataset.name or len(dataset.name.strip()) < 3:
            validation_results["issues"].append("Name is too short")
            validation_results["score"] -= 20
        
        if not dataset.description or len(dataset.description.strip()) < 10:
            validation_results["issues"].append("Description is too short")
            validation_results["score"] -= 15
        
        # Check metadata completeness
        required_metadata = ["category", "purpose", "owner"]
        missing_metadata = []
        
        for field in required_metadata:
            if not dataset.metadata.get(field):
                missing_metadata.append(field)
        
        if missing_metadata:
            validation_results["issues"].append(f"Missing metadata: {', '.join(missing_metadata)}")
            validation_results["score"] -= len(missing_metadata) * 10
        
        # Check log count
        if dataset.log_count == 0:
            validation_results["warnings"].append("Dataset is empty")
            validation_results["score"] -= 5
        elif dataset.log_count < 10:
            validation_results["warnings"].append("Dataset has very few logs")
            validation_results["score"] -= 3
        
        # Check age vs activity
        from datetime import datetime, timedelta
        created = datetime.fromisoformat(dataset.created_at.replace('Z', '+00:00'))
        updated = datetime.fromisoformat(dataset.updated_at.replace('Z', '+00:00'))
        
        if created == updated and dataset.log_count > 0:
            validation_results["warnings"].append("Dataset never updated after creation")
        
        age_days = (datetime.now(created.tzinfo) - created).days
        if age_days > 90 and updated < datetime.now(created.tzinfo) - timedelta(days=30):
            validation_results["warnings"].append("Dataset hasn't been updated in over 30 days")
        
        # Determine overall status
        if validation_results["score"] >= 90:
            validation_results["status"] = "Excellent"
        elif validation_results["score"] >= 75:
            validation_results["status"] = "Good"
        elif validation_results["score"] >= 60:
            validation_results["status"] = "Fair"
        else:
            validation_results["status"] = "Poor"
        
        print(f"Validation Results for {dataset.name}:")
        print(f"  Status: {validation_results['status']} (Score: {validation_results['score']}/100)")
        
        if validation_results["issues"]:
            print(f"  Issues:")
            for issue in validation_results["issues"]:
                print(f"    - {issue}")
        
        if validation_results["warnings"]:
            print(f"  Warnings:")
            for warning in validation_results["warnings"]:
                print(f"    - {warning}")
        
        return validation_results
        
    except Exception as e:
        print(f"Error validating dataset {dataset_id}: {e}")
        return None

# Validate a dataset
validation = validate_dataset("dataset_123456789")

Dataset Comparison

def compare_datasets(dataset_id1, dataset_id2):
    try:
        dataset1 = client.datasets.get(dataset_id1)
        dataset2 = client.datasets.get(dataset_id2)
        
        comparison = {
            "dataset1": {
                "id": dataset1.id,
                "name": dataset1.name,
                "log_count": dataset1.log_count,
                "created_at": dataset1.created_at,
                "metadata": dataset1.metadata
            },
            "dataset2": {
                "id": dataset2.id,
                "name": dataset2.name,
                "log_count": dataset2.log_count,
                "created_at": dataset2.created_at,
                "metadata": dataset2.metadata
            },
            "differences": []
        }
        
        # Compare log counts
        log_diff = abs(dataset1.log_count - dataset2.log_count)
        if log_diff > 0:
            larger = "dataset1" if dataset1.log_count > dataset2.log_count else "dataset2"
            comparison["differences"].append(
                f"Log count difference: {log_diff:,} logs ({larger} is larger)"
            )
        
        # Compare creation dates
        from datetime import datetime
        created1 = datetime.fromisoformat(dataset1.created_at.replace('Z', '+00:00'))
        created2 = datetime.fromisoformat(dataset2.created_at.replace('Z', '+00:00'))
        
        date_diff = abs((created1 - created2).days)
        if date_diff > 0:
            older = "dataset1" if created1 < created2 else "dataset2"
            comparison["differences"].append(
                f"Creation date difference: {date_diff} days ({older} is older)"
            )
        
        # Compare metadata
        metadata1_keys = set(dataset1.metadata.keys())
        metadata2_keys = set(dataset2.metadata.keys())
        
        unique_to_1 = metadata1_keys - metadata2_keys
        unique_to_2 = metadata2_keys - metadata1_keys
        common_keys = metadata1_keys & metadata2_keys
        
        if unique_to_1:
            comparison["differences"].append(
                f"Metadata unique to dataset1: {', '.join(unique_to_1)}"
            )
        
        if unique_to_2:
            comparison["differences"].append(
                f"Metadata unique to dataset2: {', '.join(unique_to_2)}"
            )
        
        # Compare common metadata values
        for key in common_keys:
            if dataset1.metadata[key] != dataset2.metadata[key]:
                comparison["differences"].append(
                    f"Different {key}: '{dataset1.metadata[key]}' vs '{dataset2.metadata[key]}'"
                )
        
        print(f"Dataset Comparison:")
        print(f"  Dataset 1: {dataset1.name} ({dataset1.log_count:,} logs)")
        print(f"  Dataset 2: {dataset2.name} ({dataset2.log_count:,} logs)")
        
        if comparison["differences"]:
            print(f"  Differences:")
            for diff in comparison["differences"]:
                print(f"    - {diff}")
        else:
            print(f"  No significant differences found")
        
        return comparison
        
    except Exception as e:
        print(f"Error comparing datasets: {e}")
        return None

# Compare two datasets
comparison = compare_datasets("dataset_123456789", "dataset_987654321")

Dataset Analytics

def analyze_dataset(dataset_id):
    try:
        dataset = client.datasets.get(dataset_id)
        
        from datetime import datetime
        created = datetime.fromisoformat(dataset.created_at.replace('Z', '+00:00'))
        updated = datetime.fromisoformat(dataset.updated_at.replace('Z', '+00:00'))
        now = datetime.now(created.tzinfo)
        
        analytics = {
            "basic_info": {
                "id": dataset.id,
                "name": dataset.name,
                "description_length": len(dataset.description) if dataset.description else 0,
                "log_count": dataset.log_count
            },
            "temporal_analysis": {
                "age_days": (now - created).days,
                "last_update_days_ago": (now - updated).days,
                "creation_date": created.strftime("%Y-%m-%d"),
                "last_update_date": updated.strftime("%Y-%m-%d"),
                "is_recently_active": (now - updated).days <= 7
            },
            "metadata_analysis": {
                "metadata_count": len(dataset.metadata),
                "has_category": "category" in dataset.metadata,
                "has_purpose": "purpose" in dataset.metadata,
                "has_owner": "owner" in dataset.metadata,
                "metadata_keys": list(dataset.metadata.keys())
            },
            "size_analysis": {
                "size_category": (
                    "empty" if dataset.log_count == 0 else
                    "small" if dataset.log_count < 100 else
                    "medium" if dataset.log_count < 1000 else
                    "large" if dataset.log_count < 10000 else
                    "very_large"
                ),
                "logs_per_day_since_creation": (
                    dataset.log_count / max((now - created).days, 1)
                )
            }
        }
        
        # Quality score calculation
        quality_score = 0
        
        # Description quality (0-25 points)
        if analytics["basic_info"]["description_length"] >= 50:
            quality_score += 25
        elif analytics["basic_info"]["description_length"] >= 20:
            quality_score += 15
        elif analytics["basic_info"]["description_length"] >= 10:
            quality_score += 10
        
        # Metadata completeness (0-25 points)
        metadata_score = 0
        if analytics["metadata_analysis"]["has_category"]:
            metadata_score += 8
        if analytics["metadata_analysis"]["has_purpose"]:
            metadata_score += 8
        if analytics["metadata_analysis"]["has_owner"]:
            metadata_score += 9
        quality_score += metadata_score
        
        # Content volume (0-25 points)
        if dataset.log_count >= 1000:
            quality_score += 25
        elif dataset.log_count >= 100:
            quality_score += 20
        elif dataset.log_count >= 10:
            quality_score += 15
        elif dataset.log_count > 0:
            quality_score += 10
        
        # Recency (0-25 points)
        if analytics["temporal_analysis"]["last_update_days_ago"] <= 7:
            quality_score += 25
        elif analytics["temporal_analysis"]["last_update_days_ago"] <= 30:
            quality_score += 20
        elif analytics["temporal_analysis"]["last_update_days_ago"] <= 90:
            quality_score += 15
        elif analytics["temporal_analysis"]["last_update_days_ago"] <= 180:
            quality_score += 10
        
        analytics["quality_score"] = quality_score
        
        print(f"Dataset Analytics: {dataset.name}")
        print(f"  Quality Score: {quality_score}/100")
        print(f"  Size: {analytics['size_analysis']['size_category']} ({dataset.log_count:,} logs)")
        print(f"  Age: {analytics['temporal_analysis']['age_days']} days")
        print(f"  Last Updated: {analytics['temporal_analysis']['last_update_days_ago']} days ago")
        print(f"  Metadata Fields: {analytics['metadata_analysis']['metadata_count']}")
        print(f"  Logs per Day: {analytics['size_analysis']['logs_per_day_since_creation']:.2f}")
        
        return analytics
        
    except Exception as e:
        print(f"Error analyzing dataset {dataset_id}: {e}")
        return None

# Analyze a dataset
analysis = analyze_dataset("dataset_123456789")

Error Handling

Comprehensive Error Handling

from keywordsai.exceptions import (
    KeywordsAIError,
    NotFoundError,
    ValidationError,
    AuthenticationError,
    RateLimitError
)
import time

def get_dataset_with_retry(dataset_id, max_retries=3):
    for attempt in range(max_retries):
        try:
            dataset = client.datasets.get(dataset_id)
            return dataset
        except NotFoundError:
            print(f"Dataset {dataset_id} not found")
            return None  # Don't retry for not found
        except AuthenticationError:
            print("Authentication failed. Check your API key.")
            return None  # Don't retry for auth errors
        except ValidationError as e:
            print(f"Validation error: {e}")
            return None  # Don't retry for validation errors
        except RateLimitError:
            if attempt < max_retries - 1:
                wait_time = 2 ** attempt  # Exponential backoff
                print(f"Rate limited. Retrying in {wait_time} seconds...")
                time.sleep(wait_time)
            else:
                print("Rate limit exceeded. Max retries reached.")
                return None
        except KeywordsAIError as e:
            print(f"API error (attempt {attempt + 1}): {e}")
            if attempt < max_retries - 1:
                time.sleep(1)
            else:
                return None
        except Exception as e:
            print(f"Unexpected error (attempt {attempt + 1}): {e}")
            if attempt < max_retries - 1:
                time.sleep(1)
            else:
                return None
    
    return None

# Use retry logic
dataset = get_dataset_with_retry("dataset_123456789")
if dataset:
    print(f"Successfully retrieved: {dataset.name}")
else:
    print("Failed to retrieve dataset")

Validation Before Use

def validate_dataset_id(dataset_id):
    """Validate dataset ID format before making API call."""
    if not dataset_id:
        return False, "Dataset ID is required"
    
    if not isinstance(dataset_id, str):
        return False, "Dataset ID must be a string"
    
    if not dataset_id.startswith("dataset_"):
        return False, "Dataset ID must start with 'dataset_'"
    
    if len(dataset_id) < 10:
        return False, "Dataset ID is too short"
    
    return True, "Valid"

def get_dataset_safely(dataset_id):
    # Validate ID format first
    is_valid, message = validate_dataset_id(dataset_id)
    if not is_valid:
        print(f"Invalid dataset ID: {message}")
        return None
    
    try:
        dataset = client.datasets.get(dataset_id)
        return dataset
    except Exception as e:
        print(f"Error retrieving dataset: {e}")
        return None

# Safe retrieval with validation
dataset = get_dataset_safely("dataset_123456789")

Best Practices

Caching Retrieved Datasets

class DatasetCache:
    def __init__(self, max_size=100, ttl_seconds=300):
        self.cache = {}
        self.max_size = max_size
        self.ttl_seconds = ttl_seconds
    
    def get(self, dataset_id):
        import time
        
        # Check if in cache and not expired
        if dataset_id in self.cache:
            dataset, timestamp = self.cache[dataset_id]
            if time.time() - timestamp < self.ttl_seconds:
                print(f"Cache hit for {dataset_id}")
                return dataset
            else:
                # Remove expired entry
                del self.cache[dataset_id]
        
        # Fetch from API
        try:
            dataset = client.datasets.get(dataset_id)
            
            # Add to cache
            if len(self.cache) >= self.max_size:
                # Remove oldest entry
                oldest_key = min(self.cache.keys(), 
                               key=lambda k: self.cache[k][1])
                del self.cache[oldest_key]
            
            self.cache[dataset_id] = (dataset, time.time())
            print(f"Cached {dataset_id}")
            return dataset
            
        except Exception as e:
            print(f"Error fetching {dataset_id}: {e}")
            return None
    
    def clear(self):
        self.cache.clear()

# Use caching
cache = DatasetCache(max_size=50, ttl_seconds=600)  # 10 minutes TTL

# First call - fetches from API
dataset1 = cache.get("dataset_123456789")

# Second call - uses cache
dataset2 = cache.get("dataset_123456789")

Performance Monitoring

import time
from functools import wraps

def monitor_performance(func):
    @wraps(func)
    def wrapper(*args, **kwargs):
        start_time = time.time()
        try:
            result = func(*args, **kwargs)
            end_time = time.time()
            duration = end_time - start_time
            print(f"{func.__name__} completed in {duration:.3f} seconds")
            return result
        except Exception as e:
            end_time = time.time()
            duration = end_time - start_time
            print(f"{func.__name__} failed after {duration:.3f} seconds: {e}")
            raise
    return wrapper

@monitor_performance
def get_dataset_monitored(dataset_id):
    return client.datasets.get(dataset_id)

# Monitor performance
dataset = get_dataset_monitored("dataset_123456789")

Common Use Cases

Dataset Health Check

def health_check_dataset(dataset_id):
    try:
        dataset = client.datasets.get(dataset_id)
        
        health_status = {
            "dataset_id": dataset.id,
            "name": dataset.name,
            "status": "healthy",
            "issues": [],
            "recommendations": []
        }
        
        # Check if dataset is empty
        if dataset.log_count == 0:
            health_status["issues"].append("Dataset is empty")
            health_status["recommendations"].append("Add logs to the dataset")
            health_status["status"] = "warning"
        
        # Check if dataset is stale
        from datetime import datetime, timedelta
        updated = datetime.fromisoformat(dataset.updated_at.replace('Z', '+00:00'))
        if datetime.now(updated.tzinfo) - updated > timedelta(days=30):
            health_status["issues"].append("Dataset hasn't been updated in 30+ days")
            health_status["recommendations"].append("Consider updating or archiving")
            if health_status["status"] == "healthy":
                health_status["status"] = "warning"
        
        # Check metadata completeness
        required_fields = ["category", "purpose"]
        missing_fields = [f for f in required_fields if f not in dataset.metadata]
        if missing_fields:
            health_status["issues"].append(f"Missing metadata: {', '.join(missing_fields)}")
            health_status["recommendations"].append("Add missing metadata fields")
        
        print(f"Health Check: {dataset.name}")
        print(f"  Status: {health_status['status'].upper()}")
        if health_status["issues"]:
            print(f"  Issues: {len(health_status['issues'])}")
            for issue in health_status["issues"]:
                print(f"    - {issue}")
        if health_status["recommendations"]:
            print(f"  Recommendations:")
            for rec in health_status["recommendations"]:
                print(f"    - {rec}")
        
        return health_status
        
    except Exception as e:
        return {
            "dataset_id": dataset_id,
            "status": "error",
            "error": str(e)
        }

# Run health check
health = health_check_dataset("dataset_123456789")

Dataset Backup Information

def get_dataset_backup_info(dataset_id):
    try:
        dataset = client.datasets.get(dataset_id)
        
        backup_info = {
            "dataset_id": dataset.id,
            "name": dataset.name,
            "backup_priority": "medium",
            "estimated_size_mb": dataset.log_count * 0.5,  # Rough estimate
            "backup_frequency": "weekly",
            "retention_period": "1_year"
        }
        
        # Determine backup priority based on size and activity
        if dataset.log_count > 10000:
            backup_info["backup_priority"] = "high"
            backup_info["backup_frequency"] = "daily"
        elif dataset.log_count > 1000:
            backup_info["backup_priority"] = "medium"
            backup_info["backup_frequency"] = "weekly"
        else:
            backup_info["backup_priority"] = "low"
            backup_info["backup_frequency"] = "monthly"
        
        # Check if dataset is critical (based on metadata)
        if dataset.metadata.get("critical") == "true":
            backup_info["backup_priority"] = "critical"
            backup_info["backup_frequency"] = "daily"
            backup_info["retention_period"] = "5_years"
        
        print(f"Backup Info: {dataset.name}")
        print(f"  Priority: {backup_info['backup_priority']}")
        print(f"  Frequency: {backup_info['backup_frequency']}")
        print(f"  Estimated Size: {backup_info['estimated_size_mb']:.1f} MB")
        print(f"  Retention: {backup_info['retention_period']}")
        
        return backup_info
        
    except Exception as e:
        print(f"Error getting backup info for {dataset_id}: {e}")
        return None

# Get backup information
backup_info = get_dataset_backup_info("dataset_123456789")